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<!DOCTYPE html> <html xmlns="http://www.w3.org/1999/xhtml"> <head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <title>Phishing URL Detection using LSTM Based Ensemble Learning Approaches</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Phishing URL Detection using LSTM Based Ensemble Learning Approaches"> <meta name="description" content="Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing is a deceitful venture with an intention to steal confidential information of an organization or an individual. Many works have been performed to build anti-phishing solutions over the years, but attackers are coming with new manoeuvres from time to time. Many of the existing techniques are experimented based on limited set of URLs and dependent on other software to collect domain related information of the URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system, we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed ensemble of LSTM models using bagging approach and stacking approach. For performing classification using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared with five different machine learning classification methods. To implement these machine learning algorithms, different URL based lexical features are extracted. Mutual Information based feature selection algorithm is used to select more relevant features to perform classifications. Both the bagging and the stacking approaches of ensemble learning using LSTM models outperform other machine learning techniques. The results are compared with other anti-phishing solutions implemented using deep learning methods. Our approaches have proved to be the more accurate one with a low false positive rate of less than 0.15% performed comparatively on a larger dataset"/> <meta name="keywords" content="Cyber security, Phishing attack, Machine learning, LSTM, Ensemble learning"/>   <meta name="dc.title" content="Phishing URL Detection using LSTM Based Ensemble Learning Approaches"> <meta name="citation_authors" content="Bireswar Banik"> <meta name="citation_authors" content="Abhijit Sarma"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.1,"> <meta name="dc.date" content="2023/01/31"> <meta name="dc.identifier" content="10.5121/ijcnc.2023.15102"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing is a deceitful venture with an intention to steal confidential information of an organization or an individual. Many works have been performed to build anti-phishing solutions over the years, but attackers are coming with new manoeuvres from time to time. Many of the existing techniques are experimented based on limited set of URLs and dependent on other software to collect domain related information of the URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system, we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed ensemble of LSTM models using bagging approach and stacking approach. For performing classification using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared with five different machine learning classification methods. To implement these machine learning algorithms, different URL based lexical features are extracted. Mutual Information based feature selection algorithm is used to select more relevant features to perform classifications. Both the bagging and the stacking approaches of ensemble learning using LSTM models outperform other machine learning techniques. The results are compared with other anti-phishing solutions implemented using deep learning methods. Our approaches have proved to be the more accurate one with a low false positive rate of less than 0.15% performed comparatively on a larger dataset."/> <meta name="dc.subject" content="Cyber security"> <meta name="dc.subject" content="Phishing attack"> <meta name="dc.subject" content="Machine learning"> <meta name="dc.subject" content="LSTM"> <meta name="dc.subject" content="Ensemble learning">   <meta name="prism.publicationName" content="International Journal of Computer Networks & Communications (IJCNC)"> <meta name="prism.publicationDate" content="2023/01/31"> <meta name="prism.volume" content="15"> <meta name="prism.number" content="1"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="17">   <meta name="citation_journal_title" content="International Journal of Computer Networks & Communications (IJCNC)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Bireswar Banik and Abhijit Sarma"> <meta name="citation_title" content="Phishing URL Detection using LSTM Based Ensemble Learning Approaches"> <meta name="citation_online_date" content="2023/01/31"> <meta name="citation_issue" content="15"> <meta name="citation_firstpage" content="17"> <meta name="citation_authors" content="Bireswar Banik"> <meta name="citation_authors" content="Abhijit Sarma"> <meta name="citation_doi" content="10.5121/ijcnc.2023.15102"> <meta name="citation_abstract_html_url" content="http://aircconline.com/abstract/ijcnc/v15n1/15123cnc02.html"> <meta name="citation_pdf_url" content="https://aircconline.com/ijcnc/V15N1/15123cnc02.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="http://aircconline.com/abstract/ijcnc/v15n1/15123cnc02.html"> <meta property="og:title" content="Phishing URL Detection using LSTM Based Ensemble Learning Approaches"> <meta property="og:description" content="Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing is a deceitful venture with an intention to steal confidential information of an organization or an individual. Many works have been performed to build anti-phishing solutions over the years, but attackers are coming with new manoeuvres from time to time. Many of the existing techniques are experimented based on limited set of URLs and dependent on other software to collect domain related information of the URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system, we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed ensemble of LSTM models using bagging approach and stacking approach. For performing classification using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared with five different machine learning classification methods. To implement these machine learning algorithms, different URL based lexical features are extracted. Mutual Information based feature selection algorithm is used to select more relevant features to perform classifications. Both the bagging and the stacking approaches of ensemble learning using LSTM models outperform other machine learning techniques. The results are compared with other anti-phishing solutions implemented using deep learning methods. Our approaches have proved to be the more accurate one with a low false positive rate of less than 0.15% performed comparatively on a larger dataset."/>   <meta name="google-site-verification" content="t8rHIcM8EfjIqfQzQ0IdYIiA9JxDD0uUZAitBCzsOIw" /> <meta name="yandex-verification" content="e3d2d5a32c7241f4" />  <style type="text/css"> a{ color:white; text-decoration:none; } ul li a{ font-weight:bold; color:#000; list-style:none; text-decoration:none; size:10px;} .imagess { height:90px; text-align:left; margin:0px 5px 2px 8px; float:right; border:none; } #left p { font-family:CALIBRI; font-size:0.90pc; margin-left: 20px; } .right { margin-right: 20px; } #button{ float: left; font-size: 17px; margin-left: 10px; height: 28px; width: 100px; background-color: #1e86c6; } </style> <link rel="icon" type="image/ico" href="../fav.ico"/> <link rel="stylesheet" type="text/css" href="../current.css" /> </head> <body> <div id="wap"> <div id="page"> <div id="top"> <table width="100%" cellspacing="0" cellpadding="0" > <tr><td colspan="3" valign="top"><img src="../top1.gif" /></td></tr> </table> </div> <div id="menu"> <a href="http://airccse.org/journal/ijcnc.html">Home</a> <a href="http://airccse.org/journal/j2editorial.html">Editorial</a> <a href="http://airccse.org/journal/j2paper.html">Submission</a> <a href="http://airccse.org/journal/j2indexing.html">Indexing</a> <a href="http://airccse.org/journal/j2special.html">Special Issue</a> <a href="http://airccse.org/journal/j2contact.html">Contacts</a> <a href="http://airccse.org" target="_blank">AIRCC</a></div> <div id="content"> <div id="left"> <h2>Volume 15, Number 1</h2> <h4 style="text-align:center;height:auto"><a>Phishing URL Detection using LSTM Based Ensemble Learning Approaches</a></h4> <h3>  Authors</h3> <p class="#left">Bireswar Banik and Abhijit Sarma, Gauhati University, India </p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing is a deceitful venture with an intention to steal confidential information of an organization or an individual. Many works have been performed to build anti-phishing solutions over the years, but attackers are coming with new manoeuvres from time to time. Many of the existing techniques are experimented based on limited set of URLs and dependent on other software to collect domain related information of the URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system, we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed ensemble of LSTM models using bagging approach and stacking approach. For performing classification using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared with five different machine learning classification methods. To implement these machine learning algorithms, different URL based lexical features are extracted. Mutual Information based feature selection algorithm is used to select more relevant features to perform classifications. Both the bagging and the stacking approaches of ensemble learning using LSTM models outperform other machine learning techniques. The results are compared with other anti-phishing solutions implemented using deep learning methods. Our approaches have proved to be the more accurate one with a low false positive rate of less than 0.15% performed comparatively on a larger dataset. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Cyber security, Phishing attack, Machine learning, LSTM, Ensemble learning. </p><br> <button type="button" id="button"><a target="blank" href="/ijcnc/V15N1/15123cnc02.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/journal/ijc2023.html">Volume 15</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li><a href="http://airccse.org/journal/jcnc_arch.html">Archives</a></li> </ul> </div><br /> <p align="center"> </p> <p align="center"> </p> </div> <div class="clear"></div> <div id="footer"><table width="100%" ><tr><td height="25" colspan="2"><br /><p align="center">® All Rights Reserved - AIRCC</p></td></table> </div> </div> </div> </div> </body> </html>